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July 25, 2020 05:44
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class LeNetClassifier: | |
def __init__(self, droprate=0.5, batch_size=128, max_epoch=300, lr=0.01): | |
self.batch_size = batch_size | |
self.max_epoch = max_epoch | |
self.lr = lr | |
self.model = LeNet(droprate) | |
self.model.cuda() | |
self.criterion = nn.CrossEntropyLoss().cuda() | |
self.optimizer = optim.SGD(self.model.parameters(), lr=lr) | |
self.loss_ = [] | |
self.test_error = [] | |
self.test_accuracy = [] | |
def fit(self, trainset, testset, verbose=True): | |
trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.batch_size, shuffle=True) | |
testloader = torch.utils.data.DataLoader(testset, batch_size=len(testset), shuffle=False) | |
X_test, y_test = iter(testloader).next() | |
X_test = X_test.cuda() | |
print(self.model) | |
for epoch in range(self.max_epoch): | |
running_loss = 0 | |
for i, data in enumerate(trainloader, 0): | |
inputs, labels = data | |
inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda() | |
self.optimizer.zero_grad() | |
outputs = self.model(inputs) | |
loss = self.criterion(outputs, labels) | |
loss.backward() | |
self.optimizer.step() | |
running_loss += loss.data[0] | |
self.loss_.append(running_loss / len(trainloader)) | |
if verbose: | |
print('Epoch {} loss: {}'.format(epoch+1, self.loss_[-1])) | |
y_test_pred = self.predict(X_test).cpu() | |
self.test_accuracy.append(np.mean(y_test == y_test_pred)) | |
self.test_error.append(int(len(testset)*(1-self.test_accuracy[-1]))) | |
if verbose: | |
print('Test error: {}; test accuracy: {}'.format(self.test_error[-1], self.test_accuracy[-1])) | |
return self | |
def predict(self, x): | |
model = self.model.eval() | |
outputs = model(Variable(x)) | |
_, pred = torch.max(outputs.data, 1) | |
model = self.model.train() | |
return pred |
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